| --- |
| language: |
| - pl |
| license: apache-2.0 |
| tags: |
| - whisper-event |
| - generated_from_trainer |
| datasets: |
| - mozilla-foundation/common_voice_11_0 |
| - google/fleurs |
| base_model: openai/whisper-small |
| model-index: |
| - name: Whisper Small PL |
| results: |
| - task: |
| type: automatic-speech-recognition |
| name: Automatic Speech Recognition |
| dataset: |
| name: mozilla-foundation/common_voice_11_0 |
| type: mozilla-foundation/common_voice_11_0 |
| config: pl |
| split: test |
| metrics: |
| - type: wer |
| value: 14.57 |
| name: WER |
| - type: wer_without_norm |
| value: 33.57 |
| name: WER unnormalized |
| - type: cer |
| value: 4.02 |
| name: CER |
| - type: mer |
| value: 14.37 |
| name: MER |
| - task: |
| type: automatic-speech-recognition |
| name: Automatic Speech Recognition |
| dataset: |
| name: facebook/voxpopuli |
| type: facebook/voxpopuli |
| config: pl |
| split: test |
| metrics: |
| - type: wer |
| value: 15.73 |
| name: WER |
| - type: wer_without_norm |
| value: 34.51 |
| name: WER unnormalized |
| - type: cer |
| value: 7.73 |
| name: CER |
| - type: mer |
| value: 15.28 |
| name: MER |
| - task: |
| type: automatic-speech-recognition |
| name: Automatic Speech Recognition |
| dataset: |
| name: google/fleurs |
| type: google/fleurs |
| config: pl_pl |
| split: test |
| metrics: |
| - type: wer |
| value: 16.79 |
| name: WER |
| - type: wer_without_norm |
| value: 35.69 |
| name: WER unnormalized |
| - type: cer |
| value: 4.99 |
| name: CER |
| - type: mer |
| value: 16.55 |
| name: MER |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # Whisper Small PL |
|
|
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 and the FLEURS datasets. |
| It achieves the following results on the evaluation set: |
| - eval_loss: 0.3571 |
| - eval_wer: 14.8004 |
| - eval_runtime: 2233.4204 |
| - eval_samples_per_second: 3.714 |
| - eval_steps_per_second: 0.232 |
| - epoch: 4.03 |
| - step: 3000 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 1e-05 |
| - train_batch_size: 24 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 48 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - training_steps: 8000 |
| - mixed_precision_training: Native AMP |
| |
| ### Framework versions |
| |
| - Transformers 4.26.0.dev0 |
| - Pytorch 1.13.0+cu117 |
| - Datasets 2.7.1.dev0 |
| - Tokenizers 0.13.2 |
| |
